It is recognized that mammography is a common method of breast cancer screening. However, analyzing mammograms requires skilled radiologists whose performance can be degraded by the demand of viewing a large number of images in a finite amount of time. Since late 90s the computer-aided detection (CAD) in mammography has been provided to the radiologists in a hope that the mammography CAD system will help the radiologists to capture true-positives (TP) that might otherwise have been overlooked.
A sizable percentage of abnormality in mammograms involves microcalcifications (mcc), i.e., tiny deposits of calcium in breast carcinoma. It is very difficult to distinguish between malignant and benign microcalcification clusters, even for experienced radiologists, which may lead to a high rate of unnecessary biopsies. (Note that an mcc cluster comprises a plurality of mcc spots each of which, in turn, comprises a plurality of mammographic image pixels.)
Therefore, it is beneficial to design the CAD algorithm in such a way that a high TP rate can be achieved while the number of false-positives (FPs) is kept to a minimum. It is discovered that some FP mcc candidates as selected by mammography CAD systems were found to fall on the linear normal structures, such as blood vessels in digital or film-based mammograms. Removing those mcc candidates that are associated with linear structures will reduce the overall FP rate. Practically, there are variations in appearance of linear structures in terms of contrast, brightness, texture and morphological shapes, among others.
Various methods for extracting linear structures have been proposed in the past. Zwiggelaar, Parr, and Taylor (R. Zwiggelaar, T. C. Parr, and C. J. Taylor, “Finding orientated line patterns in digital mammographic images,” Proc. 7th Br. Machine Vision Conf., 1996, pp. 715-724) have compared the performance of several different approaches (including orientated bin and line operator methods) to the detection of linear structures with synthetic mammographic images. Their results suggest differences between the different approaches. One approach has been implemented as a multi-scale line operator.
The work of a line operator can be described as followings. The line operator takes the average grey level of the pixels lying on an orientated local line passing through the target pixel and subtracts the average intensity of all the pixels in the locally orientated neighborhood. The line strength is compared for n orientations. Line direction is obtained from the orientation producing the maximum line strength. Scale information can be obtained by applying the line operator to images that are rescaled by Gaussian smoothing and sub-sampling. For each pixel, the scale producing the maximum line strength is taken as the detected line scale.
Cerneaz et al. (N. Cerneaz and M. Brady, “Finding Curvilinear Structures in Mammograms,” Lecture Notes in Computer Science, 905, pp. 372-382, 1995) introduce a method that estimates the intensity profile of the curvilinear structures (CLS) in mammograms in a single scale. In this method, the CLS are assumed to have circular cross section when the breast is not compressed. And the cross section of CLS in mammogram is assumed to be elliptical. Candidate pixels for CLS are detected using the response of a second order difference operation which is applied in four directions. If there is a sufficient high response for one of the orientations the pixel will form part of a CLS. A measure of line strength is obtained by determining the contrast of the line profile at these pixels. Wai et al. (A Multi-resolution CLS Detection Algorithm for Mammographic Image Analysis,” Medical Imaging Computing and Computer-Assisted Intervention, MICCAU, pp. 865-872, 2004) adopt the two step method from Cemeaz's work and devise a multi-resolution ridge detector for structures ranging from 1800 microns to 180 microns. Wai et al. also enhance the performance of the detector by using a local energy thresholding to suppress undesirable response from noise. The local energy is also used to determine CLS junctions.
Alexander Schneider et al. (U.S. Patent Application Publication No. US20020159622(A1)) propose a system and method for detecting lines in medical images. In their method, a direction image array and a line image array are formed by filtering a digital image with a single-peaked filter, convolving the regular array with second-order difference operators oriented along the horizontal, vertical, and diagonal axes, and computing the direction image arrays and line image arrays as direct scalar functions of the results of the second order difference operations. They have found that line detection based on the use of four line operator functions requires fewer computations than line detection based on the use of three line operator functions, if the four line operator functions correspond to the special orientations of 0, 45, 90 and 135 degrees.
For the issue of FP reduction, a paper by Zhang et al. (“A New False Positive Reduction Method for MCCs Detection in Digital Mammography,” Accoustics, Speech and Signal Processing 2001, Proc. IEEE Intl. Conf. on (ICASSP), V. 2, Issue 2001, pp. 1033-1036, 2001) describes a mixed feature multistage FP reduction algorithm utilizing eleven features extracted from spatial and morphology domains. These features include gray-level description, shape description and clusters description but no feature is directly related to linear structures. Wai et al. mention in their article that the results from the multi-resolution ridge detector could be beneficial to microcalcification false-positive reduction but the realization of the reduction is absent. Moreover, it is computationally inefficient to generate actual linear structures just for the purpose of confirming the association of an mcc candidate cluster with a linear structure in mammography CAD. (Note that an mcc candidate cluster is a cluster that is under testing for cancerous lesions. A cluster comprises a plurality of spots. A spot comprises a plurality of image pixels.)
Therefore, an improved general approach of microcalcification detection in mammography CAD is needed.